基于稳定风险特征选择的支付风险识别模型

Payment Risk Recognition Model Based on Stable Risk Feature Selection

在移动支付产业蓬勃发展的今天,移动支付安全成为了倍受社会关注的问题。大数据时代的到来,使得以大数据的方法来建立移动支付风险识别模型成为一种保障移动支付安全的可行方法。在使用移动支付第三方平台数据建模过程中主要面临着两个问题:首先是要保证模型的时效性和稳定性,让机器学习模型学习到最新的移动支付风险,且能够在尽量长的时间内不需要重复训练。其次,第三方支付平台的风险控制系统会基于对交易的风险判断干预多笔危险交易,产生无标签数据,如何利用这些无标签数据同样是一个要解决的问题。实验结果表明,通过比较训练集与测试集数据统计特征的方法从这些数据中筛选出平稳特征进行建模可提高模型稳定性,将无标签数据直接标注为负样本同样可以提升模型效果。

With the rapid development of mobile payment industry, the security of mobile payment has become an issue of great concern to the society. The arrival of the era of big data makes the establishment of risk identification model of mobile payment by means of big data become a feasible method to guarantee the security of mobile payment. There are two major problems in the process of using the third-party platform of mobile payment for data modeling: first, the timeliness and stability of the model should be guaranteed. Let the machine learning model learn the latest mobile payment risks, and avoid repeated training as long as possible. Secondly, the risk control system of the third-party payment platform will intervene in many dangerous transactions based on the risk judgment of transactions and generate unlabeled data. How to use these unlabeled data is also a problem to be solved. The experimental results show that the method of comparing the statistical features of training set and test set data can improve the model stability by selecting the stationary features from these data for modeling, and labeling the unlabeled data as negative samples can also improve the model effect. Both methods effectively improve the effect of the model, so as to better guarantee the security of mobile payment.